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Proceedings Paper

Music recognition system using ART-1 and GA
Author(s): Sang Moon Soak; Seok Cheol Chang; Taehwan Shin; Byung-Ha Ahn
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Paper Abstract

Previously, most optical music recognition (OMR) systems have used the neural network, and used mainly back- propagation training method. One of the disadvantages of BP is that much time is required to train data sets. For example, when new data sets are added, all data sets have to be trained. Another disadvantage is that weighting values cannot be guaranteed as global optima after training them. It means that weighting values can fall down to local optimum solution. In this paper, we propose the new OMR method which combines the adaptive resonance theory (ART-1) with the genetic algorithms (GA). For reducing the training time, we use ART-1 which classifies several music symbols. It has another advantage to reduce the number of datasets, because classified symbols through ART-1 are used as input vectors of BP. And for guaranteeing the global optima in training data set, we use GA which is known as one of the best method for finding optimal solutions at complex problems.

Paper Details

Date Published: 6 March 2002
PDF: 9 pages
Proc. SPIE 4734, Optical Pattern Recognition XIII, (6 March 2002); doi: 10.1117/12.458413
Show Author Affiliations
Sang Moon Soak, Kwangju Institute of Science and Technology (South Korea)
Seok Cheol Chang, Kwangju Institute of Science and Technology (South Korea)
Taehwan Shin, Kwangju Institute of Science and Technology (South Korea)
Byung-Ha Ahn, Kwangju Institute of Science and Technology (South Korea)

Published in SPIE Proceedings Vol. 4734:
Optical Pattern Recognition XIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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